Background The demand for regional anesthesia for major surgery has increased considerably, but only a small number of anesthesiologists can provide such care. Simulations may improve clinical performance. However, opportunities to rehearse procedures are limited, and the clinical educational outcomes prescribed by the Royal College of Anesthesiologists training curriculum 2021 are difficult to attain. Educational paradigms, such as mastery learning and dedicated practice, are increasingly being used to teach technical skills to enhance skills acquisition. Moreover, high-fidelity, resilient cadaver simulators are now available: the soft embalmed Thiel cadaver shows physical characteristics and functional alignment similar to those of patients. Tissue elasticity allows tissues to expand and relax, fluid to drain away, and hundreds of repeated injections to be tolerated without causing damage. Learning curves and their intra- and interindividual dynamics have not hitherto been measured on the Thiel cadaver simulator using the mastery learning and dedicated practice educational paradigm coupled with validated, quantitative metrics, such as checklists, eye tracking metrics, and self-rating scores. Objective Our primary objective was to measure the learning slopes of the scanning and needling phases of an interscalene block conducted repeatedly on a soft embalmed Thiel cadaver over a 3-hour period of training. Methods A total of 30 anesthesiologists, with a wide range of experience, conducted up to 60 ultrasound-guided interscalene blocks over 3 hours on the left side of 2 soft embalmed Thiel cadavers. The duration of the scanning and needling phases was defined as the time taken to perform all the steps correctly. The primary outcome was the best-fit linear slope of the log-log transformed time to complete each phase. Our secondary objectives were to measure preprocedural psychometrics, describe deviations from the learning slope, correlate scanning and needling phase data, characterize skills according to clinical grade, measure learning curves using objective eye gaze tracking and subjective self-rating measures, and use cluster analysis to categorize performance irrespective of grade. Results The median (IQR; range) log-log learning slopes were −0.47 (−0.62 to −0.32; −0.96 to 0.30) and −0.23 (−0.34 to −0.19; −0.71 to 0.27) during the scanning and needling phases, respectively. Locally Weighted Scatterplot Smoother curves showed wide variability in within-participant performance. The learning slopes of the scanning and needling phases correlated: ρ=0.55 (0.23-0.76), P<.001, and ρ=−0.72 (−0.46 to −0.87), P<.001, respectively. Eye gaze fixation count and glance count during the scanning and needling phases best reflected block duration. Using clustering techniques, fixation count and glance were used to identify 4 distinct patterns of learning behavior. Conclusions We quantified learning slopes by log-log transformation of the time taken to complete the scanning and needling phases of interscalene blocks and identified intraindividual and interindividual patterns of variability.