Prediction for not completely well classified data
You are dealing with noisy labels. I would not switch the labelling according to a trained model that learned on that particular data set, since probably you don't know which patterns lead to your models decision.

Otherwise if you know the reason for the wrong labelling, you could try to build methods yourself that run a sanity check on your data.

Nonetheless, models that don't overfit often can deal with a bit of noise in the labels. But you can also try methods that incorporate the noise aspect. Maybe check out https://stats.stackexchange.com/questions/218656/classification-with-noisy-labels