Volumetric segmentation of Optical Coherence Tomography (OCT) images is essential for diagnosing ocular diseases but requires labor-intensive voxel-wise annotations. While semi-supervised learning (SSL) can reduce annotation costs, most existing methods process data slice-by-slice, failing to exploit the inherent 3D spatial context. In this paper, we propose PC-Seg (Progressive Cross-view Segmentation), a novel curriculum learning framework that lifts sparse 2D annotations to high-precision 3D segmentation models. Unlike conventional multi-view approaches, our method employs a single 2D model to learn cross-view consistency from both standard B-scans and orthogonal slices, generating reliable volumetric pseudo-labels. These labels are then distilled into a 3D model, followed by a co-training phase where 2D and 3D models mutually refine each other through ensemble pseudo-labeling. Experiments on the MSHC dataset and the Duke DME dataset demonstrate that our method achieves accuracy comparable to fully supervised learning using only about 0.7\% of the labeled data, outperforming both state-of-the-art semi-supervised and retinal layer segmentation methods. Our code is publicly available at https://github.com/gsisaoki/pc-seg-official.