Brain-computer interface (BCI) spellers could improve access to communication for people with profound physical disabilities; however, improved speed and accuracy of these spellers is required to make them practical for everyday use. Here we introduce the combination of P300-speller confidence with the error-related potential (ErrP) to improve online single-trial error detection and correction accuracies in a BCI speller. First, we present a mechanism for obtaining P300-confidence using a real-time Bayesian dynamic stopping framework that makes novel use of additional stimuli that occur due to epoch and filter delays. Second, we propose an ensemble of decision trees to combine ErrP and P300-confidence features. Third, we describe the unique attentional differences between error and correct feedback in our spelling interface and discuss how these differences affect ErrP physiology. We tested online error detection on 11 typically developed adults using a BCI system trained on a previous day and found an average sensitivity of 86.67% and specificity of 96.59%. Automatic correction increased selection accuracy by 13.67% and utility grew by a factor of 4.48. We found, however, that the improved performance was primarily attributable to the inclusion of P300 confidence in error detection, calling into question the significance of single-trial ErrP detection.