Semantic segmentation is a fundamental task in computer vision, enabling precise scene understanding for critical applications such as autonomous driving and medical imaging. However, real-world deployment remains challenging due to the reliance on large-scale labeled datasets and the high computational cost of deep learning models. In this paper, we present a superpixel-driven active learning framework integrated with visual prompt tuning to improve semantic segmentation efficiency across diverse environments. This approach strategically selects informative and structurally significant image regions, minimizing annotation costs while preserving segmentation quality. We analyze two superpixel-based region selection strategies, standard SLIC and the computationally efficient FastSLIC, to evaluate the trade-off between segmentation accuracy and computational efficiency. In addition, prompt tuning reduces the computational burden of model adaptation, improving both training efficiency and inference speed. We evaluate our method on challenging domain adaptation benchmarks, including SYNTHIA, GTAV, and Cityscapes, demonstrating competitive performance in segmentation accuracy, annotation efficiency, and computational cost. Our results establish an effective and scalable approach for energy-efficient semantic segmentation in real-world applications.