## 1 Introduction  
Semantic segmentation, which aims to achieve pixel-level classification in images, is a fundamental task in computer vision. It is widely applied in real-world applications, including robotics, AR, VR, autonomous driving, disease diagnosis, and more. With the rise of supervised learning-based deep neural networks such as convolutional neural networks (CNNs), this method has shown impressive performance [1, 2, 3, 4]. However, it still requires a large-scale labeled dataset for training [5, 6]. When building a large dataset for training, tasks such as image classification require one label per image, but tasks such as segmentation require pixel-wise labeling, which can lead to high costs. To alleviate this problem, there have been studies on semi-supervised semantic segmentation that effectively utilize small amounts of labeled data and large amounts of unlabeled data. Recently, a semi-supervised semantic segmentation study further demonstrated the possibility of utilizing unlabeled data by showing results close to the performance of fully supervised semantic segmentation [7, 8, 9, 10]. A common solution for semi-supervised semantic segmentation is to make predictions for unlabeled data using a model trained on labeled data, and then use those predictions as pseudo-labels to enhance the model through training [11]. Since the pseudo-labels of this method are predictive results, predictive errors exist, which can have a negative effect on model training [12, 13]. A typical solution to overcome this problem is to use predicted confidence scores to filter, and use only reliable pseudo-labels with high confidence and not unreliable pseudo-labels with low confidence [14]. Current state-of-the-art (SOTA) semi-supervised semantic segmentation models are based on consistency regularization [9; 15; 16]. One example independently applied weak and strong perturbations to unlabeled images. Then, weakly augmented unlabeled image predictions with high confidence scores were assigned as pseudo-labels to strongly augmented unlabeled images, forcing consistency of output [14]. Perturbations can be applied in a variety of ways. Input perturbations can be applied using methods such as CutOut [17] and CutMix [18], as well as classical image augmentation methods such as color jitter. Feature perturbations can also be applied by injecting noise into the feature space. In particular, network perturbations are applied by encouraging consistency in the predictions of multiple models trained from different initializations. This approach showed better results than input or feature perturbation approaches [15]. Consistency regularization is based on a smoothness assumption, which means that if two input points are close in the input space, the corresponding two labels must be the same [19]. This improves generalization performance by encouraging the model to make stable predictions even with small perturbations [8]. However, this assumes perfect prediction for unlabeled data, which is difficult to achieve in practice, and this assumption includes the implication that perturbations do not push image features to the wrong side of the true decision boundary [19]. To reduce the risk of these assumptions, many previous studies have attempted to obtain high-quality pseudo-labels, based on confidence scores. Although significant results have been achieved through various pseudo-label-based approaches for unlabeled data, there has not been much exploration of what specific problems pseudo-labels have. From this perspective, we found a problem that recent SOTA models have in common: despite various approaches, pseudo-labels based on predictions for unlabeled data have common error characteristics. Figure 1a-c shows the normalized confusion matrix of pseudo-labels in semi-supervised semantic segmentation SOTA methods [7; 8; 9].