Page Stream Segmentation (PSS) is critical for automating document processing in industries like insurance, where unstructured document collections are common. This paper explores the use of large language models (LLMs) for PSS, applying parameter-efficient fine-tuning to real-world insurance data. Our experiments show that LLMs outperform baseline models in page- and stream-level segmentation accuracy. However, stream-level calibration remains challenging, especially for high-stakes applications. We evaluate post-hoc calibration and Monte Carlo dropout, finding limited improvement. Future work will integrate active learning to enhance model calibration and support deployment in practical settings.