Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation.
First, heterogeneous label vocabularies are harmonized into a common probabilistic label space, separating encoding differences from expert judgement.
Second, individual label intervals are retained and modeled with a mixture of Beta distributions trained using a proper Cram\'er-distance objective, preserving distinct expert-reported labels.
Third, we decompose predictive uncertainty into within-component, between-component, and model uncertainty, and evaluate whether these components correspond to within-label uncertainty, between-label uncertainty, and model error, respectively.
Because this correspondence is not guaranteed, we introduce decomposition matching, which aligns the predictive components to their intended label-side sources.
On sea-ice concentration the model reduces MAE by 31% over hard labels and outperforms aggregation, interval-distribution, and interval-regression baselines.