SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models

Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time. We introduce SpatialUQ, a post-hoc uncertainty method using only output probabilities. It measures the Jensen-Shannon divergence between the global prediction and the mean of five fixed spatial crops in six deterministic forward passes. The premise is simple, trustworthy predictions are spatially consistent. On NIH ChestX-ray14 (DenseNet-121, N=25,596), our Multicrop Uncertainty Score (MUS) reaches 0.784 failure-detection AUC versus 0.664 for MC-Dropout (p<10-6) at one-fifth the compute, with native calibration (SCE=0.049 vs. 0.127 for ℓ1), the best-calibrated among methods above 0.78 AUC. A supervised fusion of MUS with entropy, confidence, and ℓ1 reaches 0.832, outperforming a five-member ensemble (0.813). MUS scales with model quality, reaching 0.899 with BiomedCLIP (ρ = 0.846), while this relationship remains meaningful in-distribution (ρ = 0.523) but breaks down under severe distribution shift (VinBigData, ρ = 0.027). MUS is well-suited to diffuse findings but is less dependable for small focal lesions such as nodules. Code and experimental materials are publicly available at https://huggingface.co/datasets/kawsher11/SpatialUQ.