Explicit Adaptation Regulation for Data-Efficient Medical Image Segmentation

Data-efficient medical image segmentation requires accurate in-domain predictions and reliable transfer to unseen datasets from limited source-domain annotations. Existing approaches largely regulate adaptation through latent features, leaving the connection between the current pixel–text assessment and subsequent updates to be learned indirectly through task supervision. With supervision concentrated on the final mask, this relationship can remain weakly constrained and become dependent on source-specific patterns, limiting performance under scarce supervision and domain shift. To bridge this gap, we introduce Explicit Adaptation Regulated Segmentation (ExpARSeg), which makes local pixel-text states explicit regulators of representation adaptation and spatial prediction. Specifically, ExpARSeg employs Evidential Pixel-Text (EPT) Adapters to condition bidirectional updates within frozen vision and text encoders on local support and commitment. It further integrates Selective Spatial Rendering (SSR) to direct text-conditioned visual corrections toward low-commitment locations identified by the current margin, with initial residual responses guiding subsequent point selection. Experiments across four imaging modalities and ten held-out target datasets show that ExpARSeg improves sample efficiency and domain generalization over state-of-the-art baselines using implicit adaptation regulation.