Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning

Pathology foundation models (PFMs) offer generalizable representations for whole-slide image (WSI) analysis, yet their clinical adoption remains limited. Specifically, their predictions lack reliable confidence estimates, and no single PFM is universally best across tasks, which severely undermines trust in medical settings. To overcome this, we propose DICE, a plug-and-play framework that ensembles $K$ frozen PFMs and estimates uncertainty based on their consensus. We align the ensemble members via deep mutual learning and theoretically show that this objective controls an upper bound on epistemic uncertainty. Additionally, we demonstrate that the ensemble localizes abnormalities at the patch level without any explicit supervision. We evaluate DICE on three challenging WSI benchmarks. Notably, our framework provides reliable uncertainty estimates that accurately flag failure-prone cases under in- and out-of-distribution settings, while matching or outperforming SOTA baselines in classification, calibration, and localization. Overall, DICE takes a crucial step toward translating PFMs into uncertainty-aware decision-support systems.