Adaptive Nested-Path Conformal Prediction for Medical Image Segmentation

Reliable uncertainty quantification is essential for medical image segmentation, where missed object regions can affect downstream clinical decisions. We present Conformal Prediction Adaptive Segmentation (CPAS), a conformal prediction framework for image segmentation that constructs compact prediction sets with finite-sample coverage guarantees. Unlike confidence-thresholding methods that treat pixels independently, and morphological dilation methods, which expand masks uniformly, CPAS defines a nested expansion path that jointly exploits model confidence and spatial structure. Starting from the predicted mask, CPAS iteratively adds boundary pixels through a confidence-guided exploitation step combined with a spatial exploration mechanism, and calibrates a threshold on the accumulated uncertainty along this path. The resulting sets adapt to both the model’s soft predictions and the object geometry while preserving conformal validity. More generally, we formalize conformal calibration over input-dependent nested prediction paths. Experiments on multiple medical imaging benchmarks show that CPAS achieves the target object coverage while producing substantially more compact prediction sets than conformal segmentation baselines. On average, CPAS reduces stretch by approximately 20% compared to previous methods. These results demonstrate that confidence-guided spatial expansion is an effective approach for producing compact, statistically valid segmentation uncertainty sets.