Open-world point cloud semantic segmentation (OW-Seg) aims to predict point labels for both base and novel classes in real-world scenarios. However, existing methods depend on additional samples to support the prediction on query sample, which is labor-intensive to collect and annotate. Some methods further rely on additional learning stages to adapt to novel classes, limiting their practicality in dynamic scenarios. In addition, the intra-class distribution shifts across samples introduce biased class representations (prototypes), resulting in sub-optimal predictions. To address these limitations, we propose HOW-Seg, the first human-in-the-loop framework for OW-Seg. Instead of relying on additional samples, HOW-Seg constructs class prototypes in the query sample feature space based on sparse point-level annotations, thereby avoiding cross-sample distribution shifts. Considering the lack of granularity of initial prototypes, we introduce an interactive prototype disambiguation mechanism to refine ambiguous prototypes. To further enrich contextual awareness, we propose a prototype label assignment module, which employs a dense conditional random field (CRF) upon the prototypes to optimize their label assignments. Through iterative human feedback, HOW-Seg dynamically improves its predictions, achieving high-quality segmentation for both base and novel classes. Experiments demonstrate that with sparse annotations (e.g., one-class-one-click), HOW-Seg surpasses the state-of-the-art generalized few-shot segmentation (GFS-Seg) method under the 5-shot setting. When using advanced backbones (e.g., Stratified Transformer) and denser annotations (e.g., 10 clicks), HOW-Seg achieves 85.27% mIoU on S3DIS and 66.37% mIoU on ScanNetv2, significantly outperforming other alternatives. The source code will be publicly available at https://github.com/Pengz98/HOW-Seg.