Spectral-Distribution Uncertainty Modeling for Robust Cross-Domain Medical Image Segmentation

Domain generalization (DG) for medical image segmentation is commonly approached by simulating domain shifts through deterministic image transformations or feature perturbations. However, such methods implicitly assume that unseen domains can be approximated by predefined appearance variations. In this work, we challenge this assumption and hypothesize that medical domain shifts are more fundamentally characterized as uncertainty in spectral distributions. While anatomical structures are largely preserved across institutions, scanners, and acquisition protocols, substantial variations arise in image appearance, texture, and artifacts, which are predominantly manifested in the spectral domain. To test this hypothesis, we propose Spectral-Distribution Uncertainty (SDU), a frequency-aware DG framework that explicitly models uncertainty in the Fourier amplitude distributions of intermediate representations. By decomposing Transformer features into amplitude and phase components, SDU disentangles domain-specific spectral characteristics from domain-invariant anatomical semantics. Instead of imposing handcrafted frequency perturbations, SDU estimates spectral-distribution uncertainty during training and injects stochastic variations into the amplitude space, synthesizing diverse yet anatomically consistent representations that emulate potential unseen domains. Consequently, SDU expands the support of spectral feature distributions, enabling the model to learn representations that are robust to uncertainty-induced spectral shifts. Extensive experiments on retinal fundus and prostate MRI benchmarks demonstrate that SDU consistently outperforms state-of-the-art DG methods. Beyond empirical gains, these findings provide evidence that medical domain shifts are more appropriately characterized as uncertainty in spectral feature distributions, offering a principled perspective for generalizable medical image segmentation.