Abstract: To date, artificial intelligence has been successfully applied in various fields of material science, but these applications require a large amount of high-quality data. In practical applications, many unlabeled data points but few labeled data points can be obtained directly. The reason is that data annotations require fine and expensive experiments, and the cost of time and money cannot be ignored. Active learning can select a few high-quality samples from many unlabeled data points for labeling and use as little labeling cost as possible to optimize task model performance. However, active learning methods suitable for material attribute regression are poorly understood, and the general active learning method cannot easily avoid the negative effects of noise data, resulting in decreased costs. Therefore, we propose a new active regression learning method that includes the following features: (1) outlier detection module: using the labeled data prediction from a task model trained to fit and the labeled dataset to train the auxiliary classification model for classifying outliers and then excluding the samples that are most likely to be outliers in the unlabeled dataset; (2) greedy sampling: an iterative method is adopted to select the data farthest from the data in the labeled dataset and the selected data in the geometric space to fully consider sample diversity; and (3) minimum change sampling: selecting the unlabeled data with minimum change before and after the task model, which is trained on the labeled dataset. This part of the data is relatively lacking in the feature space of the labeled dataset. We performed experiments on the concrete slump test dataset and the negative coefficient of thermal expansion dataset and compared our method with the latest active regression learning methods. The results show that other methods do not necessarily improve task model performance after labeling data in each active learning circle on noisy datasets, and the final performance cannot reach the level of the task model trained by all data. Under the same amount of data, the performance index of the task model trained by our method is improved by 15% on average compared with other models. Because of the addition of an outlier detection mechanism, our method can effectively avoid sampling outliers when selecting high-quality samples. The task model trained using only 30%–40% of the data can achieve or even exceed the accuracy of the task model trained by all data.