They are the same in the context of convolution and deconvolution. But PSF is only one of the kernels used in convolution/deconvolution.
Besides, since it is not easy to get an accurate PSF, or probably even cannot get one, PSF sometimes is not directly used in deconvolution. Some regularization factors referring to noise is often added, as a form of wiener deconvolution.
In the presence of a poorly determined or unknown, blind deconvolution is also used with an initial guess of PSF and gradually approximating it. Note in blind deconvolution sometimes it doesn't estimate and apply P(oint)SF, but L(ine)SF in cross section acquisition (B-mode ultrasound image for example). Yet the images from most optical sensors apply PSF.
PSF is the system response to a point source, and it is mostly measured in spatial domain. When you implement the convolution/deconvolution, you may often transform the image and PSF to frequency domain to do the multiplication/dividing equivalently.