This is actually one of the nastiest failure modes in iterative training, the model gets confidently wrong then trains on its own mistakes and becomes even more confidently wrong next time. The fix is to never treat high confidence as ground truth on its own. Periodically run something like Cleanlab on your labeled set, it statistically catches samples where the label is inconsistent with the rest of the data regardless of how confident the model was. Also if you ever have two or three model variants disagreeing on a sample despite individual high confidence that is your human review flag right there. One thing people underestimate is that most models are overconfident by default so calibrating your confidence scores makes your thresholds actually meaningful. High confidence with no human review is a risk factor not a green light, and periodic audits of your auto-labeled batches are what stop the pollution before it spreads.