## 2 Related Work  
Semantic segmentation currently plays a very important role in many fields, such as medical image analysis, scene understanding, and robotic perception [17]. Although deep learning models have achieved very good performance in semantic segmentation [18], they require labeled data. The acquisition of annotations generally requires a lot of time and manpower, and many fields require experts to do the work. Therefore, considering the use of unannotated data in semantic segmentation has been investigated. Many methods based on unlabeled data have been studied to improve the performance of learning algorithms. [PERSON]. [19; 20; 21; 22; 23] achieved good results in image classification using a semi-supervised approach, consisting in assigning pseudo-labels to unlabeled data, using a model trained from a few data. [PERSON]. [24] has been a precursor in proposing to initially train one classifier on a labeled dataset in order to make predictions on an unlabeled dataset, which uses an initial small labeled dataset to train deep or shallow neural networks and other non-interpretable but high-precision models on the self-training framework, thereby obtaining an enlarged dataset, which is used to train interpretable models such as random trees. In the first stage of this method, a certain amount of labeled data is needed to obtain high accuracy based on CNN. Secondly, the simple model such as random trees has poor generalization ability. Semi-supervised segmentation approaches usually augment manually labeled training data by generating pseudo-labels for the unlabeled data and using these to generate segmentation models [25]. Consistency regularization and entropy minimization represent two prevalent strategies in using unlabeled data [26]. Consistency-based approaches [27; 28] operate on the premise that the model's output should remain stable under input perturbations. Conversely, entropy minimization [29; 30; 31] suggests that unlabeled data can be exploited in achieving clear class distinctions. Alternatively, refs. [9; 12; 32; 33] introduced methods where pseudo-labels are generated from a universal teacher model to train a problem-specific student model. Those works assume that enough annotated data are available to teach a high-quality teacher. Errors made in the early iterations can be propagated and amplified in subsequent iterations, leading to a student model that may become increasingly confident in incorrect predictions. Moreover, the model's predictions on unlabeled data may lack accuracy when the initial dataset labeled to train the teacher is not sufficiently representative of the domain targeted by the student. Although the above methods achieve good results based on labeled and unlabeled data, previous works have largely overlooked the relation between the strategy (including the selection of manually labeled samples, and the pseudo-label generator model trained from those samples) adopted to generate pseudo-labels and the benefit obtained when using those pseudo-labels (at constant average quality) to train a data-reinforced model. We propose integrating traditional machine learning methods with deep learning to address the challenge of scarce labeled data. Semi-automatic interactive approaches are considered to collect accurate labels on complex segmentation problems. Two distinct scenarios are envisioned. In the scenario depicted in Figure 1(a), the objects to segment are relatively easy to discriminate from their background, and a simple Bayesian random fern model is sufficient to capture the knowledge contained in manually annotated samples. When using the random ferns to infer pseudo-labels on new data, prior information is available about the class-label certainty/probability. As an original contribution, we propose to exploit this prior to introduce diversity among the pseudo-labels. Specifically, each time a sample is considered in a training iteration, the segmentation map of each class is defined based on a random threshold, applied to the certainty level.