Large Language Models (LLMs) can significantly enhance data annotation but often produce incorrect labels due to uncertainty. This work proposes a candidate annotation paradigm that encourages LLMs to provide multiple possible labels, utilizing a teacher-student framework called CanDist to distill these annotations into unique labels for downstream tasks. Experiments demonstrate the effectiveness of this method across various text classification challenges.