ABSTRACT Accurate delineation of brain tumor in a magnetic resonance (MR) image is crucial for its prognosis. Recently, active contour models (ACM) are increasingly being applied in brain tumor segmentation, owing to their flexibility in capturing intricate boundaries and optimization‐driven approach. However, the accuracy of these models often gets limited due to the image's intensity inhomogeneity induced false convergence and leakage through weak edged boundaries. In contrast to the traditional ACMs that use fixed or adaptive scalar weights, we propose to counter these limitations using spatially adaptive weights for the contour's regularization energy terms. This keeps the ACM independent of the weight initializations. Further, no exclusive image‐fitting term is required in its overall energy, as the spatial weighting of the regularization terms can inhibit the contour's motion near the boundary pixels. Our model dynamically adjusts the variable weight elements along the contour based on Hellinger distances of the local intensity distributions from a reference. It mitigates leakage by using a special weighting factor that checks contour motion particularly at points of changing intensity statistics. Despite the overhead caused by the local evaluation of spatial weights along the contour, implementation using parallel processing maintains a decent computational efficiency. Experimental results obtained on Cheng's brain MR dataset demonstrate the model's accuracy and robustness against various levels of inhomogeneity and boundary smoothness. Further tests on multiple other medical images highlight its generality. It outperforms the compared state‐of‐the‐art machine learning models and major ACMs.