Uncertainty-Driven Active Label Error Correction in Large-Scale Segmentation Datasets

Expanding on Rottmann & Reese’s (2022) work on uncertainty-based label error detection, this idea proposes an active learning system where uncertainty maps and component-level analysis are used not just to flag label errors, but to iteratively guide human annotators toward the most impactful corrections. After each round of correction, the model is retrained and the process repeats, gradually cleansing the dataset and improving both model and annotation quality. Unlike passive error detection, this system allocates human effort where it matters most—on ambiguous, high-uncertainty, or safety-critical regions—thereby creating cleaner, more reliable datasets for high-stakes segmentation tasks (like autonomous driving or medical imaging).

References:

1. Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification. M. Rottmann, Marco Reese (2022). IEEE Workshop/Winter Conference on Applications of Computer Vision.