Noisy-Label Calibration with Multiple Unreliable Labels

Keisuke Hanada, Futoshi Futami

In medical annotation and crowdsourcing, multiple annotators assign labels to each example, but these labels may be incorrect. Treating such noisy labels as ground truth can bias calibration evaluation. To address this problem, we propose methods for evaluating and recalibrating a fixed binary predictor under the underlying clean-label distribution, using only noisy annotations. First, given annotator-specific noise probabilities, we propose Noise Corrected ECE (NC-ECE), a binned estimator of expected calibration error (ECE) under class-conditional noise. We derive non-asymptotic bounds on the estimator's bias. Then, we propose practical algorithms for estimating unknown noise probabilities under conditional independence and label-orientation assumptions. We also develop loss-based and histogram post-hoc recalibration with clean-risk and calibration guarantees, respectively. Experiments on semi-synthetic benchmarks show that the proposed ECE estimator closely tracks its clean-label oracle counterpart and that the recalibration methods substantially improve latent-label calibration, whereas uncorrected recalibration against noisy annotations can degrade it.