When Uncertainty Fails to Localize Error: Voxel-Wise Reliability for Sparse-View CT Reconstruction

Radiative Gaussian Splatting enables fast and high-quality reconstruction in sparse-view CT, but reliable localization of reconstruction errors within the imaged anatomy remains challenging. It is unclear whether current uncertainty maps can reliably indicate where the reconstruction is wrong. We investigate when uncertainty can serve as a spatial error map and present an error-localization system that predicts voxel-wise reconstruction error by combining predictive uncertainty with complementary reconstruction-derived information. Under fixed Gaussian geometry and independent rectified-Gaussian density variables, an exact closed-form predictive moment is derived in this work to enable direct computation of voxel-wise variance without repeated reconstruction sampling. The resulting uncertainty maps were evaluated over both the whole reconstruction volume and the object foreground. Experiments show that strong whole-volume uncertainty-error correspondence can conceal poor error ranking within the object foreground area. This discrepancy recurs across three tested uncertainty constructions on a 15-scene benchmark. Guided by this analysis, our system combines closed-form predictive dispersion with image, geometry, cross-method discrepancy, and projection-residual cues through a learned error predictor. The resulting system substantially improves voxel-wise error localization within the anatomy, providing a spatially resolved approach to reconstruction reliability assessment in sparse-view CT.