Towards Integrating Uncertainty for Domain-Agnostic Segmentation

Foundation models for segmentation, such as the Segment Anything Model (SAM) family, exhibit strong zero-shot performance but remain vulnerable in shifted or limited-knowledge domains. This work investigates whether uncertainty quantification can mitigate these challenges and enhance model generalisability in a domain-agnostic manner. To this end, we (1) curate UncertSAM, a benchmark comprising eight datasets designed to stress-test SAM under challenging segmentation conditions including shadows, transparency, and camouflage; (2) evaluate a suite of lightweight, post-hoc uncertainty estimation methods; and (3) assess a preliminary uncertainty-guided prediction refinement step. Among evaluated approaches, a last-layer Laplace approximation yields uncertainty estimates that correlate well with segmentation errors, indicating a meaningful signal. While refinement benefits are preliminary, our findings underscore the potential of incorporating uncertainty into segmentation models to support robust, domain-agnostic performance.