The multi-label classification problem is a supervised learning problem that aims to predict multiple labels for each data instance. One of the key issues in designing multi-label learning approaches is how to incorporate dependencies among different labels. In this study, we propose a new approach called the interdependence model, which consists of a set of single-label predictors each of which predicts a particular label using the other labels. The proposed model can directly consider label interdependencies by reusing arbitrary conventional probabilistic models for single-label classification. We consider three prediction methods and one accelerated method for making predictions with the interdependence model. Experiments show the superior prediction performance of the proposed methods in several evaluation metrics, especially when there is a large number of candidate labels or when labels are partially given in the test phase.