Progressive Signal Calibration for Medical Image Segmentation: Diagnosing and Correcting Structural Misalignment in Training Signals

Training-signal imbalance in medical image segmentation is commonly diagnosed as a data-distribution problem, such as class frequency imbalance, foreground sparsity, or domain shift, and addressed through reweighting, sampling, or domain adaptation. We argue that this diagnosis is incomplete: a more fundamental source is structural misalignment between supervision targets and segmentation objectives. Weidentify two operative mechanisms underlying this phenomenon: gradient-mass dilution, where dominant classes consume gradient capacity needed for foreground discrimination, and inter-class supervision noise, where weak subtype boundaries generate unreliable optimization signals. On a private renal pathology dataset, four standard segmentation architectures trained under conventional four-class supervision underperform a foreground-focused supervision regime (a one-line modification of the loss) by 7.87–16.05 fg-mIoU points. The same effect replicates on three public datasets, suggesting foreground-focused supervision as a low-cost, general-purpose recipe for any pixel-imbalanced segmentation task. Building on this observation, we propose Progressive Signal Calibration (PSC), a framework that calibrates training signals at three abstraction levels. At the annotation level, PSC adaptively selects the supervision granularity that maximizes signal qual ity. At the architectural level, PSC introduces a boundary-aware feature gating mechanism for structure-sensitive feature aggregation. At the regularization level, PSC employs uncertainty- and ratio-aware constraints to stabilize optimization and prevent late-stage class-proportion drift. Importantly, PSC satisfies a con ditional gain monotonicity property: when structural misalignment is weak or absent, its regularization terms naturally attenuate, avoiding systematic degradation. Across 18 public histopathology benchmarks, PSC achieves statistically significant improvement on six datasets with no statistically significant regression on any benchmark. On the private renal pathology dataset, PSC reaches 96.0 fg-mIoU, ex ceeding the strongest matched-backbone baseline by +2.56 points under identical FG3 supervision—a substantial gain in a regime where the FG3 baseline is already above 93 fg-mIoU