In this paper, we present a novel geometry-aware pseudo-labeling framework that exploits the multi-view layout consistency of noisy estimates for self-training room layout estimation models on unseen scenes. In particular, our approach leverages a ray-casting formulation to aggregate and sample multiple estimates by considering their geometry consistency and camera proximity. As a result, our pseudo-labels can effectively leverage unseen scenes with different environmental conditions, complex room geometries, and different architectural styles without any label annotation. Results on publicly available datasets and a substantial improvement in current state-of-the-art layout estimation models show the effectiveness of our contributions.