Fires can pose an enormous risk to human safety, property, and the environment, and they tend to have devastating consequences. With the development of computer vision and deep learning technologies, fire detection and monitoring systems have increasingly begun to utilize these technologies to increase accuracy and reliability. A major bottleneck in the development of deep learning models is the need for quality large-scale datasets. Since manually annotating images on a pixel level is often time-consuming and error-prone, semi-supervised learning (SSL) offers a viable solution by leveraging a small amount of labeled data, together with a large amount of unlabeled data. One common approach when utilizing SSL is pseudo-labeling, where a model trained on an initial set of labeled data is used to generate pseudo-labels for unlabeled data, which are subsequently incorporated into a training set and used to re-train the model. This paper proposes a novel iterative mutual-training pseudo-labeling framework, along with a pseudo-label refinement technique. Experimental results obtained with models trained and evaluated on the Corsican fire dataset, indicate the effectiveness of the proposed method by achieving comparable results to a fully supervised setting for the segmentation of the fire images. The source code for this entire work is publicly available at https://github.com/Antunovic/Semi-Supervised-Pseudo-Labeling-Framework .