Deep learning models for instance segmentation have achieved remarkable success, yet their deployment in specialised domains like ecological monitoring is constrained by the prohibitive cost of acquiring high-quality polygonal annotations. This annotation dependency creates a fundamental bottleneck, limiting both scalability and adaptability of these models in real-world conservation scenarios. This paper introduces a data-centric workflow that addresses this challenge through an iterative, multi-stage active learning strategy enhanced with foundation models. The methodology integrates CLIP diversity sampling as an acquisition function with a semi-automated annotation pipeline that combines YOLOv8x6 detection proposals, human-in-the-loop verification, and SAM2-prompted segmentation refinement. A progressive training strategy using YOLOv11s-seg with quality-controlled pseudo-labelling iteratively expands the training dataset while maintaining annotation quality standards. Validated on African Penguin monitoring using Open Images V7 data and independent SANParks field data, experimental results demonstrate that CLIP diversity sampling achieves mAP \(_{50}\) of 0.82 with 400 training samples (without SAM2 refinement), compared to 0.69 for random sampling; with SAM2-refined annotations, performance reaches 0.88 mAP \(_{50}\) using the same 400 samples. Cross-domain generalisation on independent SANParks field data achieves 0.81–0.84 mAP \(_{50}\) . The framework reduces annotation requirements while providing a practical solution for deploying instance segmentation in data-scarce domains.