In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is that the CRN probes the PU and subsequently applies a Modulation and Coding Classification (MCC) technique to acquire the Modulation and Coding scheme (MCS) of the PU. This feedback is an implicit channel state information (CSI) of the PU link, indicating how harmful the probing induced interference is. The intelligence of this sequential probing process lies on the selection of the power levels of the Secondary Users (SUs) which aims to minimize the number of probing attempts, a clearly Active Learning (AL) procedure, and consequently the overall PU QoS degradation. The enhancement introduced in this work is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a univariate Bayesian Nonparamet-ric AL method, the Probabilistic Bisection Algorithm (PBA). An adaptation of the PBA is implemented for higher dimensions and its effectiveness as an uncertainty driven AL method is demonstrated through numerical simulations.