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Label aggregation such as majority voting is commonly used to resolveannotator disagreement in dataset creation. However, this may disregardminority values and opinions. Recent studies indicate that learning fromindividual annotations outperforms learning from aggregated labels, though theyrequire a considerable amount of annotation. Active learning, as an annotationcost-saving strategy, has not been fully explored in the context of learningfrom disagreement. We show that in the active learning setting, a multi-headmodel performs significantly better than a single-head model in terms ofuncertainty estimation. By designing and evaluating acquisition functions withannotator-specific heads on two datasets, we show that group-level entropyworks generally well on both datasets. Importantly, it achieves performance interms of both prediction and uncertainty estimation comparable to full-scaletraining from disagreement, while saving up to 70% of the annotation budget.

ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation