Subjective NLP tasks such as sentiment analysis and hate speech classification often involve inherent annotator disagreement, reflecting diverse perspectives shaped by annotators’ lived experiences. Although conventional approaches resolve disagreement through majority voting or aggregation, these methods risk erasing valuable nuances and minority viewpoints. Recent embedding-based and multitask models have advanced the modeling of annotator-specific judgments, yet their robustness to annotation noise remains underexplored. In this work, we systematically investigate how state-of-the-art multi-annotator learning models perform in the presence of noisy labels, and find that embedding-based approaches are more robust than aggregation and multitask baselines on datasets with many sparse annotators. Furthermore, we propose a new methodology, Noise-Robust Annotator Embedding (NRA-Embed), which integrates Robust InfoNCE (RINCE) contrastive loss to enhance models’ robustness under noisy annotation conditions. Through extensive experiments, we demonstrate that NRA-Embed effectively captures subjective disagreement while maintaining robustness to noise, achieving performance that is comparable to state-of-the-art in clean settings while providing numerically greater robustness under noise.