The study of the human decision-making process has long been a valuable field for both scientific research and practical application. Towards knowing and taking control of the decision-making process, evaluating the reliability of human decisions objectively plays an important role. Various studies have demonstrated that the confidence level of humans during the decision-making process is an important factor that reflects the correctness of decisions. In literature, several deep learning based methods have been developed to estimate decision confidence using Electroencephalography (EEG). Among these approaches, the spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) stands out. However, SST-AGCN focuses on specific subjects, and may lead to less efficiency in cross-subject situations, which are more common in application scenarios. In this paper, we propose a deep learning model called SST-AGCN with Domain Adaptation (SST-AGCN-DA) for cross-subject decision confidence estimation. To examine the effectiveness of our proposed model, we compare our SST-AGCN-DA with the original SST-AGCN, three typical domain adaption algorithms in the field, and the SST-AGCN with Domain Generalization (SST-AGCN-DG), which is another transfer learning model we developed in this paper. We conduct cross-subject confidence estimation experiments on an EEG dataset collected under a text-based decision-making task. The averaged results of leave-one-out cross-validation come out that the F1-scores of our proposed SST-AGCN-DA and SST-AGCN-DG are 79.45% and 77.04%, respectively, while the original SST-AGCN and the best of the existing domain adaptation algorithms are 74.15% and 74.25%, respectively.