Deep learning-based 3D mesh segmentation methods typically rely on dense annotations and fully supervised training, which are costly and difficult to scale to diverse scenarios. This work proposes a light-weight labeling scheme and a corresponding weakly supervised learning framework that significantly reduces annotation cost while maintaining competitive performance. First, we project pixel-level labels from 2D rendered images back onto mesh faces, generating two weakly labeled datasets. We then introduce an instance-specific label propagation method that leverages geometric and topological cues to generate dense pseudo-labels from sparse annotations. Finally, we propose a robust label learning strategy that progressively exploits increasingly reliable samples and introduces a noise-suppression loss to improve pseudo-label quality during self-training. Extensive experiments on two widely used benchmarks, i.e. , COSEG and Human Body dataset, demonstrate that our method achieves performance comparable to state-of-the-art fully supervised approaches with 2% ∼ 5% annotation cost.