Interactive segmentation for single interested object in 3D Gaussians brings new opportunity for 3D scene understanding, thanking to the real-time representation by 3D Gaussian Splatting (3DGS). However, current 3D segmentation methods on 3D Gaussians follow a two-stage framework that first reconstructs the entire scene and then performs segmentation, leading to ambiguous Gaussians near boundaries and large amount of time cost. In order to take these challenges, we introduce a joint segmentation-training framework, whose purpose is to rapidly build the accurate 3DGS representation for the interested object. Our method aims to get the multiview-consistency segmentation masks, and rapidly build the 3DGS representation based on the masks. To get the multiview-consistency masks, we use a confidence-based filtering strategy to divide the masks provided by pre-trained segmentation model, into correct masks and incorrect masks, and train the Gaussians with the correct ones to build the coarse 3DGS representation of interested object. The coarse representation is used in our mask correction process to correct the incorrect masks. In addition, to accurately express the object in scenes with complex occlusions, we use a weighted loss function to avoid the calculation of gradient in the objectively occluded area. Experiments shows that our method have the highest segmentation accuracy and the lowest time cost in single interested object, and improves robustness against inaccurate masks from pre-trained video segmentation model.