Real-world named entity recognition (NER) datasets are notorious for their noisy nature, attributed to annotation errors, inconsistencies, and subjective interpretations.Such noises present a substantial challenge for traditional supervised learning methods.In this paper, we present a new and unified approach to tackle annotation noises for NER.Our method considers NER as a constituency tree parsing problem, utilizing a tree-structured Conditional Random Fields (CRFs) with uncertainty evaluation for integration.Through extensive experiments conducted on four realworld datasets, we demonstrate the effectiveness of our model in addressing both partial and incorrect annotation errors.Remarkably, our model exhibits superb performance even in extreme scenarios with 90% annotation noise.