Efficient and accurate annotation workflows are crucial for advancing semantic segmentation, especially in waste recycling applications, where detailed labeling is time-consuming and prone to errors. This work proposes a new pipeline to accelerate the annotation process by combining the ensemble of three semantic segmentation models (UperNet, OCRNet, and DeepLabv3+) with the Segment Anything Model (SAM), for instance segmentation. Within this pipeline, we investigate two categories of approaches: (1) purely semantic segmentation-based methods–Majority Voting (MV), Max Logits (ML), and SDF Mask Retrieval (SDF-MR)–and (2) hybrid methods that integrate both semantic and instance segmentation, namely Max Logits with SAM Refinement (ML-SAM) and SDF Mask Retrieval with SAM Refinement (SDF-SAM). The work is carried out on household waste data that have been annotated with 11-class masks. The experiments demonstrated significant improvements in both annotation accuracy and efficiency compared to individual models. Integrating SAM into the system also resulted in substantially better boundary predictions and an increase in mIoU of 4.97% compared to the best-performing individual model. This approach demonstrates the potential to enhance waste recycling solutions by efficiently generating the necessary labels through the combination of semantic and instance segmentation models.