Reliability-Gated Test-Time Adaptation for Cross-Domain 2D Medical Image Segmentation

Medical image segmentation models often encounter acquisition conditions that differ from those used for training. Label-free test-time adaptation (TTA) can respond to such shifts, but entropy minimization and consistency objectives usually apply the same update pressure to every pixel. This is risky near lesion boundaries and in low-contrast regions, where an incorrect pseudo-label can reinforce the initial error. We introduce RELIABILITY-GATED TTA, a parameter-efficient TTA procedure that estimates pixel reliability from predictive entropy, alignedview disagreement, and boundary disagreement. The update is applied to reliable pixels, while an immutable source-model anchor constrains the remaining regions. Only normalization affine parameters are updated, allowing the procedure to attach to a lightweight 2D segmentation backbone. In the headline seed-7 run, Reliability-TTA lowers DDTI AURC from 0.2859 to 0.2682 and improves AURC under brightness and speckle perturbations, while Gaussian blur reverses the trend. Across the three seeds, overlap accuracy is largely unchanged on DDTI and can decrease on STU, with seed 8 changing from 0.8807 to 0.8009. These results show that spatial reliability cues can modify selective risk under some appearance shifts, but they do not provide uniform accuracy recovery. The evidence characterizes a shift-dependent operating regime for label-free 2D TTA with a normalization-affine update subspace.