Supervised models trained on community-labeled data have shown promise in Health Question Answering (HQA), but relying on “likes” as a proxy for clinical usefulness remains controversial. This work investigates the alignment between automated predictions and human perception in Portuguese HQA. Using a subset of the SaudeBR-QA corpus, we compare a Random Forest classifier against a controlled evaluation conducted by laypeople and healthcare professionals. Our results reveal a recurring divergence that we term Superficiality Bias: human evaluators frequently validate very brief answers, whereas the classifier often labels these cases as non-useful under its learned criteria. Rather than indicating that the model is inherently more clinically accurate, this pattern suggests a misalignment between community feedback and feature-driven utility judgments. We argue that crowd-based labels in medical domains should be treated cautiously and complemented with more rigorous annotation protocols.